Adaptive Rate Selection Scheme Based on Intelligent Learning Algorithm in Wireless LANs

Adaptive Rate Selection Scheme Based onIntelligent Learning Algorithm in Wireless LANs

Rongbo Zhu1 and Maode Ma2

1 College of Computer Science, South-Central University for Nationalities, MinyuanRoad 708, Wuhan 430073, China

[email protected] School of Electrical and Electronic Engineering, Nanyang Technological University,

Singapore 639798, [email protected]

Abstract. Although IEEE 802.11 wireless local area networks (WLANs)physical layers (PHYs) support multiple transmission rates, it not speci-fies how and when to switch between the permitted rates. In this paper,a novel method is introduced for rate selection in IEEE 802.11 WLANsby proposing an intelligent learning algorithm, which is simply basedon local acknowledgement frames to indicate the correct reception ofthe packet. Based on the intelligent learning algorithm, a rate selectionscheme is also proposed, which aims to improve the system throughputby adapting the transmission rate to the current link condition. Numer-ical and simulation results show that the proposed intelligent learningalgorithm is convergent quickly, and the rate selection scheme is closelyapproximates the ideal case with the perfect knowledge about the chan-nel, which significantly improves quality of service (QoS) in terms ofthroughput and delay metrics.

1 Introduction

In recent years, much interest has been devoted to the design of IEEE 802.11wireless local area networks (WLANs) [1]. IEEE 802.11 WLANs physical layers(PHYs) provide multiple transmission rates by employing different modulationand channel coding schemes. The original 802.11 standard specifies three lowspeed PHYs operating at 1 and 2 Mbps, and three high speed PHYs were ad-ditionally defined as supplements to the original standard: the 802.11b PHY [3]supporting four PHY rates up to 11 Mbps at the 2.4 GHz band, the 802.11aPHY [2] supporting eight PHY rates up to 54 Mbps at the 5 GHz band, andthe 802. 11g PHY [4] supporting 12 PHY rates up to 54 Mbps at the 2.4 GHzband. Although IEEE 802.11 WLANs support multiple transmission rates, itonly specifies which transmission rates are allowed for which types of mediumaccess control (MAC) frames, but not how and when to switch between thepermitted rates. Due to the conservative nature of the link adaptation schemesimplemented in most 802.11 devices, the current 802.11 systems are likely toshow low bandwidth utilization.

L. Kang, Y. Liu, and S. Zeng (Eds.): ISICA 2007, LNCS 4683, pp. 529–538, 2007.c© Springer-Verlag Berlin Heidelberg 2007

530 R. Zhu and M. Ma

In practice the network performance is degraded by the interference and timevarying property of the wireless channel. In order to address this problem, thereare two threads of research to improve the performance of link adaptation: Basedon accurate channel estimation and based on local acknowledgement (ACK) in-formation. For the first thread, the transmitter may estimate channel condition.Then the transmitter knows when the channel condition improves enough toaccommodate a higher rate, and adapts its transmission rate accordingly. In [5],a link adaptation scheme was presented, which is based on channel estimationwithout requiring any standard change by utilizing receiver signal strength in-dicator measurements and the number of frame retransmissions. However, sincesuch scheme operates under the assumption that all the transmission failures aredue to channel errors, it does not perform well in multiuser environments, wheremany transmissions might fail due to collisions. The authors of [6] proposed ahybrid CSI (Channel State Information) rate control scheme, which is basedon statistics approach under stable link conditions and based on SNR (Signal-to-Noise Rate) approach under volatile conditions to meet the strict latencyrequirements of streaming applications. Although the scheme is very simple andeasy to implement in devices, it is a heuristic method. In [8], a MPDU-based(MAC Protocol Data Unit) link adaptation scheme was proposed based on thetheoretical analysis, which pre-establishes a best PHY mode table by applyingthe dynamic programming technique. However, such approach requires extraimplementation effort and modifications to the current 802.11 standard. Theauthors presented a joint channel adaptive rate control and randomized schedul-ing algorithm in [7], which is performed at the MAC layer whereas the rateselection takes place at the Physical/Link (PHY/LINK) layer. However it is justfor high-speed downlink packet access networks, not suitable for WLANs. Forthe second thread, the approaches based on local ACK information. The authorsdescribed the ARF (Auto Rate Fallback) link adaptation scheme in [9], whichalternates transmission rates based on the ACK counts. If two consecutive ACKframes are not received correctly by the transmitter, the second retry of thecurrent frame and the subsequent transmissions are made at the lower rate 1Mbps and a timer is started. The timer expires or the number of successfullyreceived ACK frames reaches 10, the transmission rate is raised to 2 Mbps andthe timer is canceled. It is obviously that such scheme cannot react quickly tofast wireless channel variations for it attempts increasing the transmission rateat a fixed frequency of every 10 consecutive successful frame transmissions. Theauthors of [10] proposed an enhancement scheme of the ARF to adaptively usea short probing interval and a long probing interval to deal with the fast-fadingand slow-fading wireless channels. However, since the two probing intervals areheuristically set, this scheme works well only with certain patterns of the wirelesschannel variation. In [11], a VSVR (Variable Size and Variable Rate) scheme wasproposed, which uses a goodput regulator to maintain the committed goodputfor non-greedy applications and an optimal frame size predictor for maximiz-ing the goodput for greedy applications. Although the scheme maximizes thegoodput in a real-time manner, however it will increase rate by receiving one

Adaptive Rate Selection Scheme 531

ACK frame and decrease rate by not receiving one ACK. Such method is aheuristically method and will increase the computation burden.

From the above observations, we can see that for the approaches based onaccurate channel estimation require extra implementation effort and modifica-tions to the current 802.11 standard. On the other hand the approaches basedon the local ACK information, which are simple but just heuristic methods andlack theoretical analysis. In this paper, we blaze a novel way for rate selection inIEEE 802.11 WLANs. We propose an intelligent learning algorithm based on thealgorithm in [7], and a novel adaptive rate selection scheme is proposed which isable to adjust the transmission rate dynamically to the wireless channel varia-tion. The proposed scheme is simply based on local ACK frames to indicate thecorrect reception of the packet and is compatible with the current IEEE 802.11protocol. The key idea is to direct the transmitter’s rate-selecting attempts inan adaptive learning manner.

The rest of this paper is organized as follows. Section 2 describes the detailsof the proposed intelligent learning algorithm for adaptive rate selection. Section3 presents and assesses the simulation results. Finally, section 4 concludes thepaper.

2 Proposed Adaptive Rate Selection Scheme

In the following analyse, we just consider the IEEE 802.11a PHY for conve-nience. Actually, the similar way can be used to analyse other PHY withoutmuch difficulty. In IEEE 802.11a PHY, eight different data transmission ratesare supported by using M-ary QAM (Quadrature Amplitude Modulation). Inorder to reduce the error probability, convolutional coding and data interleavingtechniques are used for forward error correction (FEC). There are three differentcode rates used in the standard.

2.1 Bit Error Rate over AWGN Channel

The symbol error probability P (γ) with SNR γ for M-ary QAM is (M=4,16,64):

P (γ) = 1 − {1 − [2 · (1 − 1√M

) · Q(

√3

M − 1· γ)]}2. (1)

With Gary coding, we can get the BER Pe for M-ary QAM:

Pe = P (γ) · 1log2 M

. (2)

QPSK (Quadrature Phase Shift Keying) and 4-ary QAM are identical, andthe BER for BPSK (Binary Phase Shift Keying) modulation is:

Pe = Q(√

2 · γ), (3)

where the Q-function is the tail of the Gaussian distribution:


Q(x) =∫ ∞

x

1√2π

e−y2/2dy. (4)

Let PR be the BER for IEEE 802.11a with transmission rate R, With FEC,under the assumption of binary convolutional coding and hard-decision Viterbidecoding with independent errors at the channel input the union bound for PR

is [12]:

PR <∞∑

k=dfree

ckPk(γ), (5)

where dfree is the free distance of the convolutional code, ck is the total numberof error events with k bit errors, and Pk is the probability that an incorrect pathat distance k from the correct path being chosen by the Viterbi decoder.

Pk(γ) =

⎧⎪⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎪⎩

12

(ki

)(Pe)k/2(1 − Pe)k/2

+k∑

i=1+k/2

(ki

)(Pe)i(1 − Pe)k−i, k is even

k∑i=(1+k)/2

(ki

)(Pe)i(1 − Pe)k−i, k is old

(6)

2.2 Proposed Intelligent Learning Algorithm

The proposed intelligent learning algorithm maintains a control probability vec-tor to select an action among a set of actions at time. As introduced in [13],a good policy to update the probability vector is a pursuit algorithm thatalways rewards the action with the current minimum penalty estimate andthat stochastic learning control performs well in speed of convergence. In thissystem, the probability vector is the rate selection probability vector p(n) =[p1(n), ..., pK(n)], where n is the index of the sequence of transmission period.The SNR of the channel during nth transmission period is expressed by γ(n).The set of rate available are {Ri : i = 1, 2, ......, K}. At beginning the p(n) areassigned equal values:

p(0) = [1/K, ..., 1/K]. (7)

So we can get the throughput approximatively:

Si(n) = Ri[1 − Pe,i(γ(n))] (8)

where i ∈ [1, K].In order to maximize the throughput, the transmitter is required to find the

index of the best transmission rate. Such an approach requires the knowledgeof channel state during each transmission period. The intelligent learning algo-rithm presented in this paper based on the algorithm in [7], randomly selectsa transmission rate prior to transmission of a frame. The rate selection proba-bility vector is altered by an iterative updating process, which maximizes the


probability of assigning the best transmission rate. Then, the rate adaptationand transmission proceeds with the fixed p(n)until every rate is selected at leastM number of times after which p(n) is augmented at each n. Following eachtransmission, the transmitter receives an ACK signal indicating the successfulreception of the data packet. If no ACK or error ACK (NACK) is received, itindicates the packet transmission failure. The current and the past ACK signalsare used in augmenting the probability vector p(n) toward the optimum. This isdone by maintaining a time-varying estimation of throughput values S(n)′ foreach Ri. Following each transmission period, an update of S(n)′ and p(n) arecarried out considering the last M ACK signals of each rate. Then we can get [7]:

S(n)′ =Ri

M

Li(n)∑j=Li(n)−M+1

Ii(j), (9)

where Ii(j) is an indicator function:

Ii(j) =

⎧⎨⎩

1, if ACK is received

0, else(10)

Li(n) is the number of transmission periods for which the rate Ri is selectedduring the time from the start till the nth transmission period.

In order to minimize the bias against estimation of throughput values S(n)′,a factor μ is introduced to smoothen the estimated values. Then the estimationof throughput values is estimated as:

S(n)′ = μS(n)′ + (1 − μ)S(n − 1), (11)

where S(n − 1) is the last actual throughput.From (9), the intelligent learning algorithm finds the index m′ of the estimated

best rate R′m(n) maximizing the throughput S′

m(n) at time n:

m′ = arg maxi

{Ri

Li(n)∑k=Li(n)−M+1

Ii(k)}. (12)

For the probability of making the right decision, let the best rate at time n,Rm(n) be unique. Let φm(n) be the probability that the estimated best rate isthe actual best rate. Then we can get:

φm(n) = Pr{Li(n)∑

k=Li(n)−M+1

Ii(k) <Rm(n)

Ri

Lm(n)∑k=Lm(n)−M+1

Im(k)∀i �= m(n)}. (13)

The above probability is readily obtained by using binomial probability dis-tribution.


Since M ≥Li(n)∑

k=Li(n)−M+1Ii(k) ≥ 0 for all i ∈ [1, K], when

Li(n)∑k=Li(n)−M+1

Ii(k) >


Im(k), Rm(n)Ri


Im(k) may exceed M . Let’s take into

account the fact thatLi(n)∑

k=Li(n)−M+1Ii(k) < Rm(n)

Ri


Im(k) in such

cases.Let εi be the largest nonnegative integer less than α(Rm(n)/Ri), where α is

a nonnegative integer. Define the indicator function O(·) which value is 1 whencondition within parentheses is satisfied, else is 0. Define the parameter θi fori ∈ [1, K]:

θi = εi · O(εi ≤ M) + M · O(εi > M). (14)

Then we have:

φm(n) =M∑

α=1

[Pr{Lm(n)∑

k=Lm(n)−M+1

Im(k) = α}K∏

i=1,i �=m

θi∑β=0

Pr{Li(n)∑

k=Li(n)−M+1

Ii(k) = α}].

(15)Considering that:

θi = M, εi ≥ M, (16)

we can get:

φm(n) =M∑

α=1

(Mα

)Qα

m(1 − Qm)M−α ·∏i�=m

θi∑β=0

(Mβ

)Qβ

i (1 − Qi)M−β (17)

where Qi is the probability of successful transmission of a packet using the rateRi, which can be expressed as:

Qi(ηm) =∫ ∞

γ=0[1 − Pe,i(γ)f(γ|γ ∈ ηm)]dγ (18)

where i ∈ [1, K], ηm is the range of γ for which the rate Rm(n) is the optimum,f(γ|γ ∈ ηm) is the probability density function of γ conditioned on γ ∈ ηm, andit can be calculated as:

f(γ|γ ∈ ηm) = f(γ)/(∫

γ∈ηm

f(γ)dγ), γ ∈ ηm (19)

where f(γ) is the unconditional probability density function of SNR γ and canbe modelled with Rayleigh or other probability density function . Pe,i(γ) can beget form (6).

The adaptive rate selection procedure can be summarized as follows:

Step 1. If it is the first transmission period, the station initialises the prob-ability vector as in equation (7). Else the station selects a rate Ri (i ∈ [1, K])according to probability distribution pi(n).


Step 2. Update Ii(j) and Li(n) on receiving an ACK or NACK signal. Thenupdate S(n)′ according to (11).

Step 3. If for all Li(n) ≥ M for all i go to next step, else go to step 1.Step 4. Detect the index m′ of the estimated best rate and update according

to the following equations:

pi(n + 1) =

⎧⎨⎩

pi(n) − Δp, i �= m′

1 −K∑

j=1,j �=m′pi(n + 1), i = m′ (20)

where Δp is a tunable penalty probability parameter.

The convergence of the proposed algorithm can be deduced following the stepsin [7].

3 Numerical and Simulation Results

The well-known simulation tool NS-2 [15] is used to validate the proposedscheme. All the parameters used in the simulation system are as in table 1.Instantiations of the fading channel were generated using the model in [14]. Twodifferent transmission schemes are used for comparison with the proposed linkadaptation system. The first scheme is constant rate. The second scheme is thewell-known ARF [9] protocol.

Table 1. System parameters

Parameter Value

Channel rate 6,9,12,18,24,36,48,54MbpsPHY header 192 bitsACK, CTS 112 bits + PHY headerRTS 160bits + PHY headerSlot time 9 usSIFS 16usDIFS 34usPropagation Delay 1usPacket payload 1000 bytesRetryLimit 5Δp 0.08

Shown in Fig. 1 is the trend of the probability of detecting the best rate asthe selection time M , the number of ACK/NACK signals used in the estimationincreases. This curve has been derived using (15) with the transmission ratesabout 9Mbps, 12Mbps, 18Mbps and 24Mbps at 15 dB respectively. It is clearthat when M larger than 4, the probability of detecting the best rate larger than0.95, which demonstrates that the intelligent learning algorithm performs wellin speed of convergence. In the following scenarios, we set M to 4.


0 2 4 6 8 100

0.2

0.4

0.6

0.8

1

M

Pro

babi

lity

of d

etec

ting

the

best

rat

e

12Mbps

24Mbps

36Mbps

18Mbps

Fig. 1. Probability of detecting the best rate against M

0 10 20 30 40 50 600

10

20

30

40

50

Time (s)

Goo

dput

(M

bps)

Ideal rateProposed schemeFixed rateARF

Fig. 2. Goodputs of different schemes against time

In the first scenario, there is only one mobile station and access point (AP)in the WLAN and the station moves with a speed of 0.3 m/s. Fig.2 shows thegoodput of the ideal scheme, proposed scheme, fixed rate at 18 Mbps and theARF scheme.

From Fig.2, it shows that the proposed rate selection scheme can fast adapt tothe channel change. Though the channel is very good, the fixed rate can’t makeuse of the good channel state in the period of 3 to 15 second and period of 43 to60 second for it just fixed the transmission at 18 Mbps. Its goodput frequentlygoes down when the channel state change from good to bad, especially when thechannel is bad. At 22s, its goodput decreases to 2.83 Mbps. For ARF scheme, itcannot adapt to the change of the channel state as fast as the proposed scheme.The average goodput for the proposed scheme is 18.73 Mbps, which is higher


0 10 20 30 40 50

200

400

600

800

Rat

e (k

bps)

Time (s)

Proposed schemeARFFixed rate

Fig. 3. Transmission rates of different schemes against time

than that of the ARF scheme about 0.68 Mbps, and higher than that of thefixed rate 2.47 Mbps. As a result, the proposed system has the best performanceamong all the schemes.

In the second scenario, there are 20 stations around an AP, and each sta-tion transmits a 600 kbps video traffic to the AP at a speed of 0.25 m/s intime-varying channel. We observe one of the stations state. Fig. 3 shows thetransmission rates for different schemes of the selected station.

From Fig. 3, we can see that when the SNR decreases, the rate of the proposedscheme adaptively decreases quickly. Meanwhile the average rate of the proposedscheme is about 598.73 kbps, which is higher than those of fixed rate and ARFschemes about 121.64 kbps and 45.63 kbps respectively. Which demonstratesthat the proposed scheme can adaptive alter rate and highly improve the goodputof the system in time. Note that in Fig. 3, the transmission rate achieves therequested rates within first few iterations and remains at the requested values.There is no effort by the algorithm to regulate the transmission rate of thebest effort queue. Which also validates that the intelligent learning algorithm iseffective.

4 Conclusions

In this paper, we proposed a rate selection scheme based on intelligent learningalgorithm. Based on the proposed scheme we have studied the goodput perfor-mance in noise mobile environment in detail. Numerical and simulation resultsproved the validity of intelligent learning algorithm can fast detecting the bestrate and help the rate selection scheme quickly adapt to different channel statesand improve the whole system goodput. The proposed scheme also is compatiblewith the current IEEE 802.11 protocol and can be conveniently implemented inthe devices.


Acknowledgment

This work was supported in part by Natural Science Foundation of South-CentralUniversity for Nationalities under Grant YZZ07006.

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Adaptive Rate Selection Scheme Based on Intelligent Learning Algorithm in Wireless LANs